In the past three decades, snoring (affecting more than 30 % adults of the UK population) has been increasingly studied in the transdisciplinary research community involving medicine and engineering. Early work demonstrated that, the snore sound can carry important information about the status of the upper airway, which facilitates the development of non-invasive acoustic based approaches for diagnosing and screening of obstructive sleep apnoea and other sleep disorders. Nonetheless, there are more demands from clinical practice on finding methods to localise the snore sound's excitation rather than only detecting sleep disorders. In order to further the relevant studies and attract more attention, we provide a comprehensive review on the state-of-the-art techniques from machine learning to automatically classify snore sounds. First, we introduce the background and definition of the problem. Second, we illustrate the current work in detail and explain potential applications. Finally, we discuss the limitations and challenges in the snore sound classification task. Overall, our review provides a comprehensive guidance for researchers to contribute to this area.

中文翻译：

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机器学习能帮助定位打鼾声的激发吗？回顾

在过去的三十年中，涉及医学和工程学的跨学科研究团体越来越多地研究打鼾（影响超过 30% 的英国成年人）。早期工作表明，鼾声可以携带有关上呼吸道状态的重要信息，这有助于开发基于非侵入性声学的方法来诊断和筛查阻塞性睡眠呼吸暂停和其他睡眠障碍。尽管如此，临床实践对找到定位打鼾声激发的方法而不是仅仅检测睡眠障碍的方法有更多的需求。为了进一步开展相关研究并吸引更多关注，我们对从机器学习到自动分类鼾声的最新技术进行了全面回顾。第一的，我们介绍问题的背景和定义。其次，我们详细说明了当前的工作并解释了潜在的应用。最后，我们讨论了鼾声分类任务中的局限性和挑战。总体而言，我们的评论为研究人员为该领域做出贡献提供了全面的指导。